How to Set up a Business Intelligence Center of Excellence in Pharma - Best Practices

Sep 07, 2021 | Whitepaper
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How to Setup a Business Intelligence Center of Excellence in Pharma

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A Case for a Business Intelligence Center of Excellence (BI COE)

A Business Intelligence Center of Excellence (BI COE) in an organization promotes, facilitates, and delivers business reporting and analytics capabilities to support data-driven decisions. Depending on the maturity of the data/analytical culture in the company, a BI COE can be assembled with internal team members or with some external help.

A Business Intelligence competency center is a great way to kick-start a ‘culture of analytics’ in a company even when there is only a handful of reports created for business. A COE delivers benefits from the start by mobilizing reusable assets and talent to scale the function across an organization. Business teams can extract efficiency by employing focused domain workgroups and increase data trust through datagovernance practices. It will eventually evolve into an avant-garde Analytics Center of Excellence when business users start making strategic business decisions by augmenting the COE with data scientists and visual storytellers.

Commercial Analytics Excellence in Pharma

According to Gartner, a COE is appropriate to set up when the right conditions exist:

  • Specific domain knowledge is required, and but it is scarce
  • A central oversight is required, and the capability is relatively homogenous
  • There is a specific need for a focused capability within an organization, and it is required for business success

All the above is true for a commercial ops team in pharma. Within a specific pharma function such as commercial operations, the COE can share talent (people) and assets (process & technology) to maximize efficiency and collaboration. For example, in a commercial ops setting, a COE will serve business owners from sales leadership, medical affairs, patient services, payer relations, KOL managers, and Key Account Managers, and incrementally deliver high-value insights such as launch analytics, field reporting, HCP engagement, patient adherence, customer 360, etc. While the reporting and analytics needs are different, the data and visualization experts should be utilized effectively across these sub-functions. Thesetechnical teams can serve horizontally to various BUs in a pharma organization, and they can scale upto multiple geographies as well.

What Makes up a Center of Excellence?

BI Framework

A center of excellence is made up of a team of analytical and domain experts, processes that increase efficiency and technology capabilities, that churn data to extract insights. What makes up an analyticsCOE can be simplified in the framework depicted below.

A center of excellence is made up of a team of analytical and domain experts, processes that increase efficiency and technology capabilities, that churn data to extract insights. What makes up an analyticsCOE can be simplified in the framework depicted below.

Marketing_ _How_To_Set_Up_A_BI_COE_ _Best_Practices_pdf_ _All_Documents

The analytics framework consists of five key components:

  1. BI vision and strategy
  2. A governing body of policies and standards
  3. A future-ready BI architecture that supports best-in-class reporting capabilities
  4. A core team of experts and transformation leaders
  5. A collaborative work culture that supports transparency and a great feedback mechanism for continuous improvements

In a Commercial Ops setting, the COE should have executive sponsorship from the head of commercial ops or sales leadership. In a few companies, this transformational leadership has been taken up by the BU leadership (for example, by the head of diabetes or global oncology, etc.). A business partner or a business relationship manager from IT will champion the COE and provide resource fulfillment with the dataengineers and visualization experts. It is also important to think about some non-traditional roles such as business translators or visual storytellers who can serve in functional roles partnering with business leaders.

COE Operational Model as a Winning Strategy

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The success of a COE depends on its collaborative work culture. The organizational structure should intrin-sically support collaboration. A centralized COE structure might work for a small and emerging pharma. A federated model can work well for a large global organization. In a commercial operations landscape, a hub and spoke model can be effective in keeping centralized governance while providing scale and efficiency to the brands and markets.

BI Talent

Talent acquisition is a make-or-break factor when setting up an analytics organization. Talent can come from internal or external sources based on the size and scale of a company. Careful considerations in talent acquisition and skill management are required to mobilize and sustain the COE. Data analysts, data archi-tects, engineers, visualization specialists, business translators, data scientists, and business owners, all play important and focused roles in the COE. When there is a lack of talent available internally, companies hire external consultants to fill the talent gap.


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BI Platform

BI technologies have evolved over time. Many established tools have been adopted for a business-friendly reporting architecture. Self-serve BI has become a must-have in today’s business intelligence tech stack. Commercial ops reporting relies strongly on its native integration with Veeva CRM and masterdata management (MDM) systems. There are several factors involved in selecting an appropriate toolfor a collaborative business intelligence workflow. There has been a slew of new data platforms and analytics tools in the market the last few years.An evaluation of relevant tools will be a necessary step before setting up a COE, that can validate the current technology stack or evaluate opportunities for a revamp in the future.

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Enterprise BI Roadmap and Implementation Plan

The Program Management Office (PMO) should play a critical role in prioritization and resource manage-ment for the COE to function effectively. A dedicated program leader should drive business alignment on documented reporting needs and priorities. In a pharma commercial organization, it may be played by the IT Director, or a business partner, or a dedicated BI program manager.

An example of a Commercial Ops BI roadmap in pharma

Strong BI-driven organizations will have a clearly defined BI roadmap. The commercial or sales ops teams need to develop a business reporting roadmap that might look like the following.

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How to Make Your BI Journey Into a Corporate Success Story

Analytics is a Top-down Game

Business intelligence vision should start from the office of the CXO and trickle down to BU functions. Creating a business intelligence COE with a focus on delivering and promoting an analytics-driven business should be a strategic corporate priority. Companies that fail to engage executive leadership and lacksponsorship, will eventually fail, or become mediocre in leveraging the competitive advantage of BI.

“Clearly, culture depends in large part on the orientation of senior leaders, and especially the CEO. There is little doubt that a CEO’s own reliance on data – or lack thereof – in decision making and improving thebusiness sends a powerful message to the rest of the organization.” – How CEOs Can Lead a Data-driven Culture•Thomas H. Davenport & Nitin Mittal

Governance Saves the Day, Every Time

Most in pharma grapple with data challenges due to the dynamics of HCP (healthcare professionals) affiliations or quality of data entry in their CRM systems. Quite a few companies struggle with managing constant requests from brand teams to slice and dice their data to constantly fine-tune their sales strategies to improve sales performance or market share or HCP brand loyalty. A reporting COE must keep up with this barrage of challenges by employing diligent data and BI governance in place.A good governance setup is like an insurance policy. It protects the organization against risks of data corruption, security, data loss, and poor data quality management. It also ensures that the organization reuses its assets and talents within the COE. Itreduces costs and increases the efficiency of deliveringvalue in the shortest time without exposing the organization to data mishandling.

There are two kinds of governance – data and analytics governance. Both are interconnected to deliver immense business benefits.

A) Data Governance

  • Ensures secure role-based access to data
  • Set data usage guidelines across the organization
  • Provides trusted, accessible, searchable, and clean data for anyone in the organization
  • One definition of data across the company

B) Analytics Governance

  • Fit-for-purpose analytics
  • Balances priorities, demands, and feasibilities
  • Approval workflows
  • Reusable assets and talents – efficient & zero waste

Collaboration is the Final Act

Analytics is not IT. Analytics is not business. Analytics is not for data scientists. Delivering business value to the company is the common denominator for IT and business stakeholders involved. It can only be done through a highly integrated, hybrid team structure. Business leaders, analysts, data architects, datascientists, and visual storytellers should collaborate in a seamless workflow to maximize efficiency in delivering insights for driving strategic business decisions.

It is also important to devise an effective communication strategy to share quick wins and success stories of analytics-driven business outcomes across the organization. Celebrating success promotes a ‘culture of analytics’ across the organization and encourages knowledge sharing.

Conclusion

According to CIO magazine, “Analytics success starts with a center of excellence”. While it might seem to be an arduous task, setting up an analytics COE is a reasonably achievable feat with a clear strategy and methodology. If the common pitfalls discussed in the guide above are avoided, the analytics COE delivers business results, and it also becomes a strategic competitive advantage for a company. With a clearly defined set of business use cases, a strong team of experts, and a business leader, the success of settingup an analytics COE is within reach.

DataZymes helps pharma organizations transform large amounts of data into actionable and value-driven insights. Learn more about our services here.

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